Overview

Dataset statistics

Number of variables11
Number of observations998
Missing cells1256
Missing cells (%)11.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory138.4 KiB
Average record size in memory142.0 B

Variable types

Numeric9
Categorical1
Unsupported1

Alerts

BMI (kg/m²) is highly overall correlated with SexHigh correlation
Sex is highly overall correlated with BMI (kg/m²)High correlation
ldl_cholesterol_mmol_L_consolidated is highly overall correlated with total_cholesterol_mmol_LHigh correlation
total_cholesterol_mmol_L is highly overall correlated with ldl_cholesterol_mmol_L_consolidatedHigh correlation
study_week has 998 (100.0%) missing valuesMissing
albumin_g_dL has 56 (5.6%) missing valuesMissing
total_protein_g_dL has 69 (6.9%) missing valuesMissing
total_cholesterol_mmol_L has 26 (2.6%) missing valuesMissing
hdl_cholesterol_mmol_L_consolidated has 26 (2.6%) missing valuesMissing
ldl_cholesterol_mmol_L_consolidated has 26 (2.6%) missing valuesMissing
triglycerides_mmol_L_consolidated has 26 (2.6%) missing valuesMissing
fasting_glucose_mmol_L has 24 (2.4%) missing valuesMissing
study_week is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-11-25 06:48:40.936878
Analysis finished2025-11-25 06:48:44.314603
Duration3.38 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Age (at enrolment)
Real number (ℝ)

Distinct29
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.599198
Minimum41
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:44.419734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile44
Q149
median53
Q359
95-th percentile63
Maximum71
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9694709
Coefficient of variation (CV)0.11137239
Kurtosis-0.97067221
Mean53.599198
Median Absolute Deviation (MAD)5
Skewness0.064769631
Sum53492
Variance35.634583
MonotonicityNot monotonic
2025-11-25T08:48:44.462171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5363
 
6.3%
5561
 
6.1%
4857
 
5.7%
6256
 
5.6%
4955
 
5.5%
5052
 
5.2%
5252
 
5.2%
4750
 
5.0%
5446
 
4.6%
6046
 
4.6%
Other values (19)460
46.1%
ValueCountFrequency (%)
414
 
0.4%
424
 
0.4%
4318
 
1.8%
4430
3.0%
4539
3.9%
4639
3.9%
4750
5.0%
4857
5.7%
4955
5.5%
5052
5.2%
ValueCountFrequency (%)
711
 
0.1%
681
 
0.1%
671
 
0.1%
663
 
0.3%
658
 
0.8%
6416
 
1.6%
6336
3.6%
6256
5.6%
6140
4.0%
6046
4.6%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
Male
501 
Female
497 

Length

Max length6
Median length4
Mean length4.995992
Min length4

Characters and Unicode

Total characters4986
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male501
50.2%
Female497
49.8%

Length

2025-11-25T08:48:44.511182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T08:48:44.547683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male501
50.2%
female497
49.8%

Most occurring characters

ValueCountFrequency (%)
e1495
30.0%
a998
20.0%
l998
20.0%
M501
 
10.0%
F497
 
10.0%
m497
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3988
80.0%
Uppercase Letter998
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1495
37.5%
a998
25.0%
l998
25.0%
m497
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
M501
50.2%
F497
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin4986
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1495
30.0%
a998
20.0%
l998
20.0%
M501
 
10.0%
F497
 
10.0%
m497
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4986
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1495
30.0%
a998
20.0%
l998
20.0%
M501
 
10.0%
F497
 
10.0%
m497
 
10.0%

BMI (kg/m²)
Real number (ℝ)

High correlation 

Distinct816
Distinct (%)82.2%
Missing5
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean29.560846
Minimum15.24
Maximum65.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:44.586091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.24
5-th percentile18.286
Q123.53
median29.07
Q334.52
95-th percentile42.696
Maximum65.89
Range50.65
Interquartile range (IQR)10.99

Descriptive statistics

Standard deviation7.7932914
Coefficient of variation (CV)0.2636356
Kurtosis0.67413325
Mean29.560846
Median Absolute Deviation (MAD)5.49
Skewness0.63362711
Sum29353.92
Variance60.735391
MonotonicityNot monotonic
2025-11-25T08:48:44.635203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.473
 
0.3%
34.763
 
0.3%
31.263
 
0.3%
32.573
 
0.3%
30.943
 
0.3%
26.963
 
0.3%
28.553
 
0.3%
27.853
 
0.3%
30.53
 
0.3%
37.23
 
0.3%
Other values (806)963
96.5%
(Missing)5
 
0.5%
ValueCountFrequency (%)
15.241
0.1%
15.31
0.1%
15.391
0.1%
15.461
0.1%
15.691
0.1%
15.851
0.1%
15.931
0.1%
16.141
0.1%
16.211
0.1%
16.341
0.1%
ValueCountFrequency (%)
65.891
0.1%
62.841
0.1%
58.621
0.1%
56.421
0.1%
56.091
0.1%
53.911
0.1%
53.851
0.1%
53.131
0.1%
52.891
0.1%
51.371
0.1%

study_week
Unsupported

Missing  Rejected  Unsupported 

Missing998
Missing (%)100.0%
Memory size15.6 KiB

albumin_g_dL
Real number (ℝ)

Missing 

Distinct42
Distinct (%)4.5%
Missing56
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean41.72293
Minimum29.5
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:44.678517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum29.5
5-th percentile37
Q140
median42
Q343.5
95-th percentile46
Maximum60
Range30.5
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.0735208
Coefficient of variation (CV)0.073665027
Kurtosis3.1810547
Mean41.72293
Median Absolute Deviation (MAD)2
Skewness0.32038988
Sum39303
Variance9.44653
MonotonicityNot monotonic
2025-11-25T08:48:44.725112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41118
11.8%
42109
10.9%
4395
 
9.5%
4072
 
7.2%
4460
 
6.0%
4550
 
5.0%
3950
 
5.0%
3849
 
4.9%
41.537
 
3.7%
40.533
 
3.3%
Other values (32)269
27.0%
(Missing)56
 
5.6%
ValueCountFrequency (%)
29.51
 
0.1%
301
 
0.1%
312
 
0.2%
31.51
 
0.1%
32.51
 
0.1%
331
 
0.1%
343
 
0.3%
357
 
0.7%
35.51
 
0.1%
3618
1.8%
ValueCountFrequency (%)
601
 
0.1%
581
 
0.1%
561
 
0.1%
541
 
0.1%
522
 
0.2%
502
 
0.2%
495
0.5%
48.53
0.3%
486
0.6%
47.53
0.3%

total_protein_g_dL
Real number (ℝ)

Missing 

Distinct868
Distinct (%)93.4%
Missing69
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean55.019494
Minimum12.09
Maximum261.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:44.772926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum12.09
5-th percentile24.886
Q137.15
median49.1
Q366.7
95-th percentile100.98
Maximum261.25
Range249.16
Interquartile range (IQR)29.55

Descriptive statistics

Standard deviation26.854133
Coefficient of variation (CV)0.48808398
Kurtosis7.6577044
Mean55.019494
Median Absolute Deviation (MAD)14.07
Skewness2.0273365
Sum51113.11
Variance721.14448
MonotonicityNot monotonic
2025-11-25T08:48:44.822114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.723
 
0.3%
32.773
 
0.3%
54.923
 
0.3%
65.152
 
0.2%
59.782
 
0.2%
30.582
 
0.2%
44.922
 
0.2%
42.072
 
0.2%
35.452
 
0.2%
62.492
 
0.2%
Other values (858)906
90.8%
(Missing)69
 
6.9%
ValueCountFrequency (%)
12.091
0.1%
12.991
0.1%
14.381
0.1%
16.251
0.1%
17.441
0.1%
17.521
0.1%
18.021
0.1%
18.141
0.1%
18.151
0.1%
18.411
0.1%
ValueCountFrequency (%)
261.251
0.1%
209.41
0.1%
194.441
0.1%
184.781
0.1%
184.581
0.1%
174.971
0.1%
167.661
0.1%
166.311
0.1%
162.31
0.1%
150.851
0.1%

total_cholesterol_mmol_L
Real number (ℝ)

High correlation  Missing 

Distinct369
Distinct (%)38.0%
Missing26
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean4.3946914
Minimum1.6
Maximum11.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:44.868903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile2.8555
Q13.68
median4.35
Q35.02
95-th percentile6.1745
Maximum11.98
Range10.38
Interquartile range (IQR)1.34

Descriptive statistics

Standard deviation1.0298543
Coefficient of variation (CV)0.23434052
Kurtosis2.8989069
Mean4.3946914
Median Absolute Deviation (MAD)0.67
Skewness0.72446762
Sum4271.64
Variance1.0605998
MonotonicityNot monotonic
2025-11-25T08:48:44.919387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1111
 
1.1%
4.0410
 
1.0%
4.759
 
0.9%
3.758
 
0.8%
4.458
 
0.8%
5.427
 
0.7%
3.557
 
0.7%
4.377
 
0.7%
4.097
 
0.7%
4.087
 
0.7%
Other values (359)891
89.3%
(Missing)26
 
2.6%
ValueCountFrequency (%)
1.61
0.1%
1.81
0.1%
1.821
0.1%
21
0.1%
2.151
0.1%
2.181
0.1%
2.21
0.1%
2.281
0.1%
2.331
0.1%
2.351
0.1%
ValueCountFrequency (%)
11.981
0.1%
7.821
0.1%
7.711
0.1%
7.61
0.1%
7.431
0.1%
7.31
0.1%
7.241
0.1%
7.21
0.1%
7.091
0.1%
7.082
0.2%

hdl_cholesterol_mmol_L_consolidated
Real number (ℝ)

Missing 

Distinct175
Distinct (%)18.0%
Missing26
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1.3130247
Minimum0.49
Maximum3.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:44.964099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile0.85
Q11.05
median1.245
Q31.47
95-th percentile2.06
Maximum3.24
Range2.75
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.38605468
Coefficient of variation (CV)0.29401936
Kurtosis3.5288924
Mean1.3130247
Median Absolute Deviation (MAD)0.205
Skewness1.4660611
Sum1276.26
Variance0.14903822
MonotonicityNot monotonic
2025-11-25T08:48:45.010913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0718
 
1.8%
1.1417
 
1.7%
1.0517
 
1.7%
1.0917
 
1.7%
1.1517
 
1.7%
1.1316
 
1.6%
1.0315
 
1.5%
1.4415
 
1.5%
1.0815
 
1.5%
1.2615
 
1.5%
Other values (165)810
81.2%
(Missing)26
 
2.6%
ValueCountFrequency (%)
0.491
 
0.1%
0.611
 
0.1%
0.621
 
0.1%
0.641
 
0.1%
0.652
0.2%
0.662
0.2%
0.672
0.2%
0.683
0.3%
0.691
 
0.1%
0.712
0.2%
ValueCountFrequency (%)
3.241
 
0.1%
3.141
 
0.1%
3.131
 
0.1%
3.111
 
0.1%
3.091
 
0.1%
2.973
0.3%
2.791
 
0.1%
2.581
 
0.1%
2.521
 
0.1%
2.52
0.2%

ldl_cholesterol_mmol_L_consolidated
Real number (ℝ)

High correlation  Missing 

Distinct341
Distinct (%)35.1%
Missing26
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean2.6102778
Minimum0.03
Maximum9.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:45.059933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile1.2
Q11.96
median2.59
Q33.18
95-th percentile4.259
Maximum9.74
Range9.71
Interquartile range (IQR)1.22

Descriptive statistics

Standard deviation0.93922499
Coefficient of variation (CV)0.35981802
Kurtosis3.1099517
Mean2.6102778
Median Absolute Deviation (MAD)0.61
Skewness0.58581124
Sum2537.19
Variance0.88214359
MonotonicityNot monotonic
2025-11-25T08:48:45.109166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.712
 
1.2%
2.378
 
0.8%
2.418
 
0.8%
3.028
 
0.8%
2.168
 
0.8%
2.398
 
0.8%
2.097
 
0.7%
2.37
 
0.7%
2.917
 
0.7%
2.367
 
0.7%
Other values (331)892
89.4%
(Missing)26
 
2.6%
ValueCountFrequency (%)
0.031
0.1%
0.141
0.1%
0.151
0.1%
0.181
0.1%
0.211
0.1%
0.262
0.2%
0.271
0.1%
0.281
0.1%
0.331
0.1%
0.451
0.1%
ValueCountFrequency (%)
9.741
0.1%
5.551
0.1%
5.361
0.1%
5.221
0.1%
5.121
0.1%
5.061
0.1%
4.991
0.1%
4.981
0.1%
4.881
0.1%
4.831
0.1%

triglycerides_mmol_L_consolidated
Real number (ℝ)

Missing 

Distinct207
Distinct (%)21.3%
Missing26
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1.0413786
Minimum0.22
Maximum10.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:45.156407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.42
Q10.64
median0.85
Q31.21
95-th percentile2.1545
Maximum10.42
Range10.2
Interquartile range (IQR)0.57

Descriptive statistics

Standard deviation0.74350213
Coefficient of variation (CV)0.71395949
Kurtosis38.239011
Mean1.0413786
Median Absolute Deviation (MAD)0.255
Skewness4.6665268
Sum1012.22
Variance0.55279542
MonotonicityNot monotonic
2025-11-25T08:48:45.205206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8217
 
1.7%
0.8117
 
1.7%
0.7116
 
1.6%
0.6616
 
1.6%
0.6915
 
1.5%
0.8314
 
1.4%
0.5414
 
1.4%
0.6713
 
1.3%
0.6513
 
1.3%
0.7813
 
1.3%
Other values (197)824
82.6%
(Missing)26
 
2.6%
ValueCountFrequency (%)
0.221
 
0.1%
0.251
 
0.1%
0.281
 
0.1%
0.32
 
0.2%
0.322
 
0.2%
0.334
0.4%
0.344
0.4%
0.356
0.6%
0.362
 
0.2%
0.375
0.5%
ValueCountFrequency (%)
10.421
0.1%
7.241
0.1%
5.62
0.2%
5.451
0.1%
5.241
0.1%
5.131
0.1%
5.011
0.1%
4.541
0.1%
4.41
0.1%
4.061
0.1%

fasting_glucose_mmol_L
Real number (ℝ)

Missing 

Distinct360
Distinct (%)37.0%
Missing24
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean5.3995483
Minimum3.13
Maximum29.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2025-11-25T08:48:45.252773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.13
5-th percentile3.88
Q14.5125
median5.03
Q35.59
95-th percentile8.1415
Maximum29.76
Range26.63
Interquartile range (IQR)1.0775

Descriptive statistics

Standard deviation1.9540223
Coefficient of variation (CV)0.36188625
Kurtosis40.469736
Mean5.3995483
Median Absolute Deviation (MAD)0.54
Skewness5.1699169
Sum5259.16
Variance3.8182031
MonotonicityNot monotonic
2025-11-25T08:48:45.302096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0310
 
1.0%
4.759
 
0.9%
4.999
 
0.9%
4.968
 
0.8%
5.168
 
0.8%
5.198
 
0.8%
4.878
 
0.8%
4.278
 
0.8%
5.057
 
0.7%
5.027
 
0.7%
Other values (350)892
89.4%
(Missing)24
 
2.4%
ValueCountFrequency (%)
3.131
0.1%
3.211
0.1%
3.371
0.1%
3.421
0.1%
3.441
0.1%
3.451
0.1%
3.471
0.1%
3.481
0.1%
3.51
0.1%
3.552
0.2%
ValueCountFrequency (%)
29.761
0.1%
20.211
0.1%
20.031
0.1%
19.781
0.1%
17.71
0.1%
16.791
0.1%
16.441
0.1%
14.441
0.1%
14.371
0.1%
14.191
0.1%

Interactions

2025-11-25T08:48:43.813853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.010383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.472019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.782945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.201782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.522391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.838557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.162675image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.486721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.850336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.073037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.507332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.909081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.240334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.559658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.876106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.198227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.525908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.883015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.127650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.539747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.943394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.273511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.594576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.909855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.232826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.561770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.918812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.197107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.573502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.979236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.309071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.632317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.945801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.267748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.599669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.952670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.286800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.608770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.013647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.342689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.666107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.981840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.303096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.635221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.989319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.324184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.644560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.050422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.378773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.699539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.017381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.338468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.671383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:44.025299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.363407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.678713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.092543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.415237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.734466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.053483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.375458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.707202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:44.060889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.399216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.714819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.130934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.450934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.768899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.089808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.409911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.743406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:44.096565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.434861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:41.748698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.167165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.484517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:42.804191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.126069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.445583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T08:48:43.777911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T08:48:45.335504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)Sexalbumin_g_dLfasting_glucose_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidatedtotal_cholesterol_mmol_Ltotal_protein_g_dLtriglycerides_mmol_L_consolidated
Age (at enrolment)1.0000.1200.138-0.0440.105-0.0280.1010.091-0.1390.045
BMI (kg/m²)0.1201.0000.507-0.1850.241-0.3110.2360.111-0.1340.134
Sex0.1380.5071.0000.2630.0360.1370.2210.1520.2680.165
albumin_g_dL-0.044-0.1850.2631.0000.0670.0970.1070.1910.0950.193
fasting_glucose_mmol_L0.1050.2410.0360.0671.000-0.1810.0590.065-0.0150.298
hdl_cholesterol_mmol_L_consolidated-0.028-0.3110.1370.097-0.1811.0000.0350.3040.002-0.255
ldl_cholesterol_mmol_L_consolidated0.1010.2360.2210.1070.0590.0351.0000.895-0.0220.139
total_cholesterol_mmol_L0.0910.1110.1520.1910.0650.3040.8951.000-0.0060.270
total_protein_g_dL-0.139-0.1340.2680.095-0.0150.002-0.022-0.0061.0000.104
triglycerides_mmol_L_consolidated0.0450.1340.1650.1930.298-0.2550.1390.2700.1041.000

Missing values

2025-11-25T08:48:44.143551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T08:48:44.209612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T08:48:44.273051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Age (at enrolment)SexBMI (kg/m²)study_weekalbumin_g_dLtotal_protein_g_dLtotal_cholesterol_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidatedtriglycerides_mmol_L_consolidatedfasting_glucose_mmol_L
98562.0Female26.78NaN43.065.815.581.553.281.647.59
98654.0Female29.52NaN43.567.052.910.661.591.464.44
98762.0Female17.77NaN43.066.704.041.542.270.504.32
98854.0Female20.45NaN44.0100.924.281.422.500.804.22
98958.0Female33.99NaN40.075.005.221.173.640.906.76
99058.0Female23.46NaN41.0NaN4.621.602.690.734.30
99160.0Female21.40NaN35.052.662.871.070.815.244.64
99259.0Female20.78NaN41.543.693.070.901.571.313.47
99353.0Female21.30NaN43.5100.033.861.182.310.814.10
99459.0Female26.00NaN54.057.295.561.053.921.304.61
Age (at enrolment)SexBMI (kg/m²)study_weekalbumin_g_dLtotal_protein_g_dLtotal_cholesterol_mmol_Lhdl_cholesterol_mmol_L_consolidatedldl_cholesterol_mmol_L_consolidatedtriglycerides_mmol_L_consolidatedfasting_glucose_mmol_L
197354.0Male35.84NaN42.034.925.832.073.350.915.05
197460.0Male27.86NaN41.570.454.611.322.960.735.65
197553.0Male35.00NaNNaN75.51NaNNaNNaNNaNNaN
197653.0Male26.79NaN41.030.484.371.003.020.784.68
197754.0Male39.20NaN41.027.373.441.131.940.816.48
197846.0Male26.31NaN41.036.344.241.462.570.473.71
197957.0Male36.79NaN43.036.194.410.972.741.555.11
198050.0Male37.20NaNNaN32.773.771.022.390.804.86
198143.0Male34.99NaN38.556.354.561.492.840.504.26
198256.0Male33.87NaN41.035.866.751.484.761.125.68